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magnitudedev/magnitude
Open source inference engine that tunes its kernels on your hardware to find the fastest way to run each model. 2x faster than Ollama. Works on Apple Silicon, NVIDIA, AMD, or just a CPU.
Current momentum
Stars
7K
+349 since sync
Commits
100
in period
Active contributors
4
+0%
Issues closed
4
406.9h avg. resolution
Recorded trajectory
Movement you can verify over time.
Loading daily momentum history.
Behind the number
A score you can inspect.
Each signal contributes points to the total. These are capped GitHub samples, not complete activity counts or proof of adoption.
Development activity40.0 / 40 pts
100 commits · 11 opened PRs · 4 closed issues in 7 days
24 + 22 × ln(1 + weekly weighted activity). Activity = commits + 2 × opened PRs + closed issues. Signal bounded to 0–100, then weighted 40%.
Contributor breadth16.1 / 25 pts
4 sampled contributors in 7 days · 0% contributor-growth input
24 + 25 × ln(1 + contributors × √(7 / days)) + 0.15 × growth. Growth uses the 30-day windows; capped samples use a neutral 0 input. Signal bounded to 0–100, then weighted 25%.
Issue resolution5.4 / 20 pts
406.9 hours resolution input
105 − 9 × log₂(1 + hours). Uses sampled closed issues, not first-response time. With no resolved issues, the model uses 168 hours. Signal bounded to 0–100, then weighted 20%.
Release cadence15.0 / 15 pts
1 days cadence input
105 − 5 × cadence days. With fewer than two published releases, the model uses time since the last push as a proxy. Signal bounded to 0–100, then weighted 15%.
What changed in the recorded sample?
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482 forks
Downstream adoption
48 open issues
Community demand
1 day cadence
Shipping consistency